About
We're building the
reasoning layer
AI was missing.
Recora started from a simple observation: AI tools are excellent at generating plausible text. They are terrible at generating trustworthy outputs. The difference matters enormously when the document in front of you is a contract, a compliance framework, or an agreement worth millions of dollars.
The existing approaches — chatbots, RAG pipelines, AI assistants — all share the same fundamental design: pass text to an LLM, ask for a response, hope it's accurate. For consumer applications, this is fine. For professional and operational use cases, it is insufficient.
We built Recora as cognitive infrastructure: a structured pipeline that constrains LLMs to operate only on validated, schema-typed data. The result is not smarter AI — it is accountable AI. Every output can be audited. Every claim can be traced. Every artifact can be trusted.
7-stage
Deterministic reasoning pipeline
0
Ungrounded assertions in any output
100%
Source-cited artifacts
LLM-agnostic
Works with any model
Vision
From probabilistic AI to deterministic reasoning systems.
The next decade of AI infrastructure will not be won by the model that generates the most convincing text. It will be won by the systems that make AI outputs trustworthy enough to build operational decisions on.
Recora is being built as that infrastructure layer — a cognitive operating system that can sit beneath any domain, any workflow, any organization that needs AI to be accountable, not just capable.
Principles
Determinism over probability
We believe AI outputs should be verifiable, not just plausible. Every Recora artifact is traceable to source.
Infrastructure, not interface
Recora is not a product you talk to. It is a reasoning layer that operates beneath your workflows.
Evidence before assertion
No claim exists in a Recora output without a source. This is not a feature — it is an architectural constraint.
Domain expertise encoded, not described
Domain Packs capture how experts in a field actually think — not just what they know.
Early access is open for select use cases.
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